Entropy最新文献

Root cause analysis is used to find the specific fault location and cause of a fault during system fault diagnosis. It is an important step in fault diagnosis. The root cause analysis method based on causality starts from the origin of the causal connection between transactions and infers the location and cause of the mechanism failure by analyzing the causal impact of variables between systems, which has methodological advantages. Causal analysis methods based on transfer entropy are proven to have biases in calculation results, so there is a phenomenon of calculating false causal relationships, which leads to the problem of insufficient accuracy in root cause analysis. Liang-Kleeman information flow (LKIF) is a kind of information entropy that can effectively carry out causal inference, which can avoid obtaining wrong causal relationships. We propose a root cause analysis method that combines graphical lasso and information flow. In view of the large amount of redundant information in industrial data due to the coupling effect of industrial systems, graphical lasso (Glasso) is a high-precision dimensionality reduction method suitable for large-scale and high-dimensional datasets. To ensure the timeliness of root cause analysis, graphical lasso uses dimensionality reduction of the data. Then, LKIF is used to calculate the information flow intensity of each relevant variable, infer the causal relationship between the variable pairs, and trace the root cause of the fault. On the Tennessee Eastman simulation platform, root cause analysis was performed on all faults, and two root cause analysis solutions, transfer entropy and information flow, were compared. Experimental results show that the LKIF-Glasso method can effectively detect the root cause of faults and display the propagation of faults throughout the process. It further shows that information flow has a better effect in root cause analysis than transfer entropy. And through the root cause analysis of the step failure of the stripper, the reason why information flow is superior to transfer entropy is explained in detail.

Strong approximation, introduced by Strassen (1964), is one of the most powerful methods to prove limit theorems in probability and statistics. In this paper we use strong approximation of lacunary series with conditionally independent sequences to prove uniform and permutation-invariant limit theorems for such series.

Interactions between excitatory and inhibitory neurons in the cerebral cortex give rise to different regimes of activity and modulate brain oscillations. A prominent regime in the cortex is the inhibition-stabilized network (ISN), defined by strong recurrent excitation balanced by inhibition. While theoretical models have captured the response of brain circuits in the ISN state, their connectivity is typically hard-wired, leaving unanswered how a network may self-organize to an ISN state and dynamically switch between ISN and non-ISN states to modulate oscillations. Here, we introduce a mean-rate model of coupled Wilson-Cowan equations, link ISN and non-ISN states to Kolmogorov-Sinai entropy, and demonstrate how homeostatic plasticity (HP) allows the network to express both states depending on its level of tonic activity. This mechanism enables the model to capture a broad range of experimental effects, including (i) a paradoxical decrease in inhibitory activity, (ii) a phase offset between excitation and inhibition, and (iii) damped gamma oscillations. Further, the model accounts for experimental work on asynchronous quenching, where an external input suppresses intrinsic oscillations. Together, findings show that oscillatory activity is modulated by the dynamical regime of the network under the control of HP, thus advancing a framework that bridges neural dynamics, entropy, oscillations, and synaptic plasticity.

With the rapid development of quantum computing technology, traditional cryptographic systems are facing unprecedented challenges. Post-Quantum Cryptography (PQC), as a new cryptographic technology that can resist attacks from quantum computers, has received widespread attention in recent years. This paper first analyzes the threat of quantum computing to existing cryptographic systems, then introduces in detail the main technical routes of PQC and its standardization process. Then, a testing and evaluation system for PQC is proposed and relevant tests are carried out. Finally, suggestions for future development are put forward.

Industrial linkages play a crucial role in sustaining industrial agglomerations, driving economic growth, and shaping the spatial architecture of economic systems. This study explores the complexity of causal networks within the industrial ecosystems of China and the United States, using high-frequency economic data to compare the interdependencies and causal structures across key sectors. By employing the partial cross mapping (PCM) technique, we capture the dynamic interactions and intricate linkages among industries, providing a detailed analysis of inter-industry causality. Utilizing data from 32 Chinese industries and 11 United States industries spanning 2015 to 2023, our findings reveal that the United States, as a global leader in technology and finance, exhibits a diversified and service-oriented industrial structure, where financial and technology sectors are pivotal to economic propagation. In contrast, China's industrial network shows higher centrality in heavy industries and manufacturing sectors, underscoring its focus on industrial output and export-led growth. A comparative analysis of the network topology and resilience highlights that China's industrial structure enhances network stability and interconnectivity, fostering robust inter-industry linkages, whereas the limited nodal points in the United States network constrain its resilience. These insights into causal network complexity offer a comprehensive perspective on the structural dynamics and resilience of the economic systems in both countries.

In this work, we consider the waveform design for radar systems based on orthogonal time-frequency space (OTFS). The conditional mutual information (CMI), chosen as a promising metric for assessing the radar cognitive capability, serves as the criterion for OTFS waveform design. After formulating the OTFS waveform design problem based on maximizing CMI, we propose an equivalent waveform processing approach by minimizing the autocorrelation sidelobes and cross-correlations (ASaCC) of the OTFS transmitting matrix. Simulation results demonstrate that superior performance in target information extraction is achieved by the optimized OTFS waveforms compared to random waveforms.

This paper investigates the joint optimization of active and passive beamforming in simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted non-orthogonal multiple access (NOMA) systems, with the aim of maximizing system throughput and improving overall performance. To achieve this goal, we propose an iterative and efficient algorithmic framework. For active beamforming optimization, the fractional programming (FP) method is employed to reformulate the non-convex optimization problem into a convex problem, making it more tractable. Additionally, Nesterov's extrapolation technique is introduced to enhance the convergence rate and reduce computational overhead. For the phase optimization of the STAR-RIS, a binary phase design method is proposed, which reformulates the binary phase optimization problem as a segmentation problem on the unit circle. This approach enables a closed form solution that can be derived in linear time. Simulation results demonstrate that the proposed algorithmic framework outperforms existing benchmark algorithms in terms of both system throughput and computational efficiency, verifying its effectiveness and practicality in STAR-RIS-assisted NOMA systems.

With more China railway business information systems migrating to the China Railway Cloud Center (CRCC), the attack surface is expanding and there are increasing security threats for the CRCC to deal with. Cyber Mimic Defense (CMD) technology, as an active defense strategy, can counter these threats by constructing a Dynamic Heterogeneous Redundancy (DHR) architecture. However, there are at least two challenges posed to the DHR deployment, namely, the limited number of available schedulable heterogeneous resources and memorization-based attacks. This paper aims to address these two challenges to improve the CRCC-DHR reliability and then facilitate the DHR deployment. By reliability, we mean that the CRCC-DHR with the limited number of available heterogeneous resources can effectively resist memorization-based attacks. We first propose three metrics for assessing the reliability of the CRCC-DHR architecture. Then, we propose an incomplete-information-based game model to capture the relationships between attackers and defenders. Finally, based on the proposed metrics and the captured relationship, we propose a redundant-heterogeneous-resources scheduling algorithm, called the Entropy Weight Scheduling Algorithm (REWS). We evaluate the capability of REWS with the three existing algorithms through simulations. The results show that REWS can achieve a better reliability than the other algorithms. In addition, REWS demonstrates a lower time complexity compared with the existing algorithms.

Characterized by frequent earthquakes and a dense population, Yunnan Province, China, faces significant seismic hazards and is a hot place for earthquake forecasting research. In a previous study, we evaluated the performance of the b value for 5-year seismic forecasting during 2000-2019 and made a forward prediction of M ≥ 5.0 earthquakes in 2020-2024. In this study, with the forecast period having passed, we first revisit the results and assess the forward prediction performance. Then, the background seismicity rate, which may also offer valuable long-term forecasting information, is incorporated into earthquake prediction for Yunnan Province. To assess the effectiveness of the prediction, the Molchan Error Diagram (MED), Probability Gain (PG), and Probability Difference (PD) are employed. Using a 25-year catalog, the spatial b value and background seismicity rate across five temporal windows are calculated, and 86 M ≥ 5.0 earthquakes as prediction samples are examined. The predictive performance of the background seismicity rate and b value is comprehensively tested and shown to be useful for 5-year forecasting in Yunnan. The performance of the b value exhibits a positive correlation with the predicted earthquake magnitude. The synergistic effect of combining these two predictors is also revealed. Finally, using the threshold corresponding to the maximum PD, we integrate the forecast information of background seismicity rates and the b value. A forward prediction is derived for the period from January 2025 to December 2029. This study can be helpful for disaster preparedness and risk management in Yunnan Province, China.

Recently, several papers identified technical issues related to equivalent time-domain and frequency-domain "characterization of the n-block or transmission" feedback capacity formula and its asymptotic limit, the feedback capacity, of additive Gaussian noise (AGN) channels, first introduce by Cover and Pombra in 1989 (IEEE Transactions on Information Theory). The main objective of this paper is to derive new results on the Cover and Pombra characterization of the n-block feedback capacity formula, and to clarify the main points of confusion regarding the time-domain results that appeared in the literature. The first part of this paper derives new equivalent time-domain sequential characterizations of feedback capacity of AGN channels driven by non-stationary and non-ergodic Gaussian noise. It is shown that the optimal channel input processes of the new equivalent sequential characterizations are expressed as functionals of a sufficient statistic and a Gaussian orthogonal innovations process. Further, the Cover and Pombra n-block capacity formula is expressed as a functional of two generalized matrix difference Riccati equations (DREs) of the filtering theory of Gaussian systems, contrary to results that appeared in the literature and involve only one DRE. It is clarified that prior literature deals with a simpler problem that presupposes the state of the noise is known to the encoder and the decoder. In the second part of this paper, the existence of the asymptotic limit of the n-block feedback capacity formula is shown to be equivalent to the convergence properties of solutions of the two generalized DREs. Further, necessary and or sufficient conditions are identified for the existence of asymptotic limits, for stable and unstable Gaussian noise, when the optimal input distributions are asymptotically time-invariant but not necessarily stationary. This paper contains an in-depth analysis, with various examples, and identifies the technical conditions on the feedback code and state space noise realization, so that the time-domain capacity formulas that appeared in the literature, for AGN channels with stationary noises, are indeed correct.